The Coolest Data Science And Machine Learning Tool Companies Of The 2020 Big Data 100

Part 5 of CRN’s Big Data 100 looks at the vendors solution providers need to know in the data science and machine learning software space.

Learning Curve

Before businesses can begin to take advantage of the growing volumes of big data being generated today, data scientists and developers have to develop the underlying machine learning algorithms and predictive models that support the business analytics applications and processes used by business analysts and information workers.

Traditionally that work has been a time-consuming process. But a new generation of data science and machine learning platforms is automating much of that work, making it possible for businesses and organizations to leverage their big data assets more quickly for competitive advantage.

As part of the 2020 Big Data 100, we’ve put together a list of data science and machine learning tool companies – from established vendors to those in startup mode – that solution providers should be aware of.

This week CRN is running the Big Data 100 list in slide shows with vendors of business analytics software, big data systems and platforms, database systems, data management and integration tools, and data science and machine learning tools. (Some vendors offer big data products that span multiple technology categories: They appear in the slideshow for the technology segment in which they are most prominent.)


Top Executive: Peter Wang

Anaconda’s data science platform is based on the increasingly popular Python programming language for building applications and custom deep-learning models. Targeted use cases include predictive analytics, neural networks, machine learning, data visualization and bias mitigation.

Anaconda, based in Austin, offers Individual, Team and Enterprise editions of its software, along with related professional services.

Big Squid

Top Executive: CEO Chris Knoch

Big Squid’s Kracken is a self-service, automated machine learning workbench that the company says brings AI and machine learning capabilities to business users. The platform automates many tasks usually performed by data scientists, helping analysts understand their data and build predictive and prescriptive analytical applications. Personal audio equipment maker Skullcandy, for example, uses Big Squid machine learning applications to predict product return rates, identify failing parts and improve manufacturing.

Big Squid is based in Salt Lake City.


Top Executive: CEO Florian Douetteau

The Dataiku collaborative data science and machine learning platform is used by data scientists, data analysts and developers to design, prototype, build and deploy data-centric analytical applications.

Earlier this month New York-based Dataiku released Dataiku 7 with deeper integration with Microsoft 365 services including Teams, SharePoint and OneDrive.


Top Executive: CEO Jeremy Achin

DataRobot’s machine learning platform is used to automate every step of developing, deploying and maintaining predictive model systems at scale. Earlier this month Boston-based DataRobot announced enhancements to the platform including Visual AI for developing apps for computer vision use cases, the ability to turn any machine learning model into an AI application, and new automated deep-learning capabilities

Domino Data Lab

Top Executive: CEO Nick Elprin

Domino Data Lab’s data science platform is used by large enterprises, including Allstate, Dell Technologies and Instacart, to develop model-driven applications that help run their business. The system automates DevOps steps for data science projects, including developing, validating, delivering and monitoring models, freeing up data scientists for research and testing ideas.

The software runs in the cloud, on-premises or in hybrid environments. Domino Data Lab is based in San Francisco.


Top Executive: CEO Ryohei Fujimaki

DotData touts its DotData Enterprise machine learning and data science platform as capable of reducing AI and business intelligence projects from months to days. The platform is based on the company’s AutoML 2.0 engine that provides full-cycle automation of data science and machine learning tasks.

Startup DotData, launched in 2018 and based in San Mateo, Calif., raised $23 million in Series A funding in October 2019.

Top Executive: CEO Sri Ambati creates the H20 open-source AI and machine learning system for AI transformation initiatives with the goal to “democratize AI for everyone.” Targeted applications include advanced analytics, fraud detection, claims management and digital advertising. The company sells the H20 Driverless AI commercial edition and Sparkling Water, an H20 machine learning engine for Spark systems., based in Mountain View, Calif., raised $72.5 million in financing in August 2019.


Top Executive: CEO Asaf Somekh

The Iguazio Data Science Platform automates and accelerates machine learning workflow pipelines, allowing businesses to develop, deploy and manage AI applications at scale that improve business outcomes – what the company calls “MLOps.”

In January New York-based Iguazio raised $24 million in financing.


Top Executive: CEO Michael Berthold

KNIME (Konstanz Information Miner) offers two core products: The open-source KNIME data analytics platform for building data science workflows and the commercial KNIME Server for “productizing” data science applications. The server product offers team collaboration capabilities, workflow automation, and the ability to deploy, manage and monitor analytical applications.

KNIME began offering its software on AWS in January. Based in Zurich, Switzerland, KNIME’s U.S. headquarters is in Austin.


Top Executive: Peter Lee

RapidMiner’s data science and machine learning platform unifies data preparation, machine learning and model deployment tasks. A key differentiator is the platform’s RapidMiner Studio visual workflow designer that uses a drag-and-drop interface to create predictive models. Other components include the RapidMiner Go autoML tool and RapidMiner Server for sharing and reusing predictive models, automating processes and deploying models into production.

In February the company, headquartered in Boston, launched RapidMiner 9.6 with capabilities that improve collaboration between users of different skill levels.